The 3rd Generation Partnership Project (3GPP) is responsible for the standardization of the UMTS (Universal Mobile Telecommunication Service) system and LTE (Long Term Evolution) is currently under discussion as a next generation mobile communication system of the UMTS system. LTE is a technology for realizing high-speed packet-based communication that can reach high data rates both in the downlink and in the uplink. The 3GPP work on LTE is also referred to as E-UTRAN (Evolved Universal Terrestrial Access Network). Thus work is ongoing in 3GPP to specify an evolution to UTRAN, denoted E-UTRA, as part of the LTE effort. The first release of LTE, referred to as release-8 (Rel-8) can provide peak rates of 300 Mbps, a radio-network delay of e.g. 5 ms or less, a significant increase in spectrum efficiency and a network architecture designed to simplify network operation, reduce cost, etc. In order to support high data rates, LTE allows for a system bandwidth of up to 20 MHz. LTE is also able to operate in different frequency bands and can operate in at least FDD (Frequency Division Duplex) and TDD (Time Division Duplex). The modulation technique or the transmission scheme used in LTE is known as OFDM (Orthogonal Frequency Division Multiplexing).
For the next generation mobile communications system e.g. IMT-advanced (International Mobile Telecommunications) and/or LTE-advanced, which is an evolution of LTE, support for bandwidths of up to 100 MHz is being discussed. One issue with such wide bandwidth is that it is challenging to find free 100 MHz of contiguous spectrum, due to that radio spectrum is a limited resource.
LTE-advanced can be viewed as a future release of the LTE standard and since it is an evolution of LTE, backward compatibility is important so that LTE-advanced can be deployed in spectrum already occupied by LTE. This means that for an LTE user equipment or a LTE terminal, a LTE-advanced capable network can appear as a LTE network. In both LTE and LTE-advanced radio base stations known as eNBs or eNodeBs—where e stands for evolved—, multiple antennas with beamforming technology can be adopted in order to provide high data rates to user equipments. Thus, LTE and LTE-advanced are an example of MIMO (Multiple-Input, Multiple-Output) radio systems. Another example of a MIMO based system is WiMAX (Worldwide Interoperability for Microwave Access) system.
In MIMO based systems, beamforming or precoding is used for supporting multi-layer transmissions. Precoding involves among other features, focusing the power and direction of transmit antennas to improve the perceived SNR (Signal to Noise Ratio) or the perceived SINR (Signal to Interference and Noise Ratio) and thus improving the performance and throughput in the system. Precoding can be categorized as codebook and non-codebook based precoding. Codebook based precoding, which maybe used for LTE FDD, has been introduced in order to minimize the feedback overhead which can be significant without careful design of the feedback link (i.e. uplink) from a user equipment to a radio base station. A codebook is a predetermined set of precoding matrices wherein each precoding matrix is identified by a PMI (precoding matrix index (or indicator)). Therefore, when a user equipment requests the establishment of a channel or a connection with a radio base station, the user equipment estimates the quality of the channel and instead of feeding back a complete channel matrix to the radio base station, the user equipment only selects and sends back the index of the most suitable precoding matrix out of a codebook. This way, the feedback overhead is reduced.
For non-codebook based precoding (or beamforming), the radio base station is not constrained to select precoding matrix(ces) from a predefined set of precoding matrices, but can instead exploit channel reciprocity to adjust the downlink transmission weights from channel estimates obtained from uplink transmissions. Examples of uplink transmissions include user equipment DRS (dedicated reference signal(s)) also used for fast data demodulation besides wideband CRS (common reference signal(s)) for channel measurement. Since DRS is/are dedicated per user equipment, DRS is/are carried in allocated/assigned physical resource block (PRBs) assigned by the radio base station to the user equipment. Furthermore, in contrast to codebook-based precoding, in case of non-codebook based precoding, the user equipment makes estimation of the overall beamformed channel. To enable this, LTE e.g. provides the possibility for the transmission of user equipment reference symbols, being transmitted using the same beam-forming as the user data, and allowing for the user equipment to estimate the overall beam-formed channel. Another aspect of this is that the number of transmit antennas used for non-codebook based precoding is not constrained by the number of available cell-specific reference signals.
It should be mentioned that in a codebook-based precoding system, wideband CRS is also used for data demodulation together with the PMI informed by the radio base station via downlink control signalling. Therefore, the user equipment has different demodulation strategies depending on the precoding scheme. A comparison between two kinds of precoding for CRS respectively DRS and associated reporting indexes/indicators such as RI (Rank Indicator); PMI and CQI (Channel Quality Indicator) are shown in Table 1 of FIG. 1. Note that DRS is not carried when codebook based precoding is used.
As mentioned earlier, for non-codebook based precoding, channel estimation is performed based on DRS in the allocated PRBs. However, due to that the user equipment lacks knowledge on precoding granularity, the easiest strategy is to perform per-PBR channel estimation, which is also applied in current standardization evaluations. FIG. 2 is an example depicting a conventional strategy on channel estimation for non-codebook based precoding. As shown, each subframe, according to the scheduling strategy of the radio base station (here denoted eNB as an example), includes PRBs (6 PRBs are shown per subframe) and each PRB is subject to a downlink precoder (beamforming) denoted Wi and each subframe has different precoding granularity. As an example, PRB #1 of subframe 1 is subject to downlink precoder W1, PRB #2 and PRB #3 of the same subframe #1 are instead subject to downlink precoder W2; PRB #1 and PRB#2 of subframe #2 are subject to precoder W1 whereas PRB #3 and PRB #4 of subframe #2 are subject to precoder W2 etc. The channel estimation strategy applied by the user equipment (UE) is, as shown, performed per-PRB. Subframes, as shown scheduled in the time and frequency domain.
Referring to FIGS. 3A-3B, there are illustrated the performance evaluations (i.e. simulations) when per-PRB channel estimation is performed for dual-layer beamforming under two ETU (Extended Typical Urban) scenarios, the first one (FIG. 3A) where the speed of the UE is 3 km/h, and the second one (FIG. 3B) where the speed of the UE i 120 km/h. The performance of ideal channel estimations is also depicted for the two scenarios. The legends in respective graph indicate which curves relate to ideal channel estimations and which curves relate to realistic channel estimation for a ETU scenario. The simulations show the BLER (BLock Error Rate) as a function of the SNR (in dB). Throughout the simulations, precoding granularity of 4 PRBs is used. Furthermore, the UE:s channel estimator used is based on the criterion of the minimum mean square error (MMSE) using a 2 dimensional-MMSE filter (2D-MMSE). In addition, 3 modulations schemes are considered: QPSK (Quadrature Phase Shift Keying), 16 QAM (Quadrature Amplitude Modulation) and 64 QAM; and the same coding rate equal to ½ is applied for all modulations schemes. As shown, regardless of the ETU scenario the gap between ideal channel estimation and per-PRB realistic channel estimation is large. The gaps are encircled in FIGS. 3A-3B. The higher the modulation scheme and the higher the mobility or speed of the user equipment, the larger the gap. This is because higher modulation e.g. 64 QAM is more sensitive to channel errors. As an example, with reference to FIG. 3A where the speed is 3 km/h one can see that for 64 QAM, the BLER is approximately 20% when the SNR is equal to 13 dB for an ideal channel. For the same 64 QAM and for the same SNR value, the BLER is approximately 45% for a realistic channel. With reference to FIG. 3B where the speed is 120 km/h one can see that for 64 QAM, the BLER is approximately 50% when the SNR is equal to 13 dB for an ideal channel. For the same 64 QAM and for the same SNR value, the BLER is approximately 90% for a realistic channel.
Thus, the drawback of adopting conventional channel estimation schemes is that the demodulation performance of beamforming and the performance of the system in general are far away from ideal performance, especially for higher modulation schemes.